WO2021244510A1 - Yield prediction method in integrated circuit wafer manufacturing - Google Patents
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- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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Definitions
- the invention relates to a method for predicting yield in integrated circuit wafer manufacturing.
- the yield of new products has a key impact on the initial cost budget and output value.
- the semiconductor integrated circuit wafer manufacturing involves complex manufacturing procedures and manufacturing There are many processes and many types of products. Among them, there is a large degree of uncertainty and subjective empirical factors in the prediction of new product yield. Due to the inability to accurately predict the production yield of new products, the decision-making time in the early stage of production is prolonged. Increasing the time cost of production and other related costs, and leading to the inability to accurately estimate the corresponding output value before the decision to develop new products and put into production.
- the present invention is carried out in order to solve the above-mentioned problems. It provides a yield prediction method in integrated circuit wafer manufacturing. Based on the functional relationship between random defect density and yield in wafer manufacturing, refer to the mature products on the production line. Through the establishment of data model and regression analysis, the yield data of new products can reach a relatively accurate prediction value of the yield of new products, which provides a method of predicting the yield of new products for semiconductor integrated circuit wafer manufacturing.
- the present invention proposes a yield prediction method in integrated circuit wafer manufacturing, which has such characteristics and includes the following steps:
- Obtaining candidate reference product models selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
- Candidate reference product prediction From each of the revised predicted yield models based on the current candidate reference product, the predicted yield of each other candidate reference product is obtained, and the predicted yield of each other candidate reference product is obtained according to the second preset rule.
- the overall yield error value of the product is obtained according to the third preset rule based on the correlation coefficient of the linear function of the full coordination factor of each candidate reference product;
- Final reference product selection select the candidate reference product with the smallest overall yield error value or the linear function correlation coefficient of the full coordination factor closest to 1 as the final reference product;
- New product prediction model acquisition obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
- New product yield prediction According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
- the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the first preset rule is:
- Ye is the actual yield of the candidate reference product e
- ⁇ e is the average number of defects of the wafer corresponding to the candidate reference product e.
- the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the yield impact factor includes the number of mask layers, chip area, and minimum line width of the reference product.
- the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the full adjustment factor linear regression equation ⁇ is:
- b is a constant term
- X 1 , X 2 , and X 3 are variable terms related to the yield influencing factors, respectively E N ratio corresponding to the variable terms reticle mask layers current product and the reference product of the number of layers N, Is the variable item corresponding to the ratio of the chip area A of the current product to the chip area A e of the reference product,
- the corresponding minimum line width W of the variable terms refer to product a minimum line width W E of the current product
- k 1 X 1 corresponds to a variable linear regression coefficients
- k 3 Is the linear regression coefficient corresponding to the variable X 3.
- the yield prediction method in integrated circuit wafer manufacturing also has the feature that the linear function of the full coordination factor is:
- the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the modified yield prediction model is:
- the yield prediction method in integrated circuit wafer manufacturing also has the feature that the second preset rule is: the overall yield error value d based on the current candidate reference product is
- N is the total quantity of other candidate reference products
- z i is the predicted yield of other candidate reference products i
- ⁇ is the total average of the differences of all the other alternatives for each prediction refer to product yield and the actual yield.
- the yield prediction method in integrated circuit wafer manufacturing also has the feature that the third preset rule includes: a linear function correlation coefficient R based on the full coordination factor of each candidate reference product for
- the yield prediction method in integrated circuit wafer manufacturing also has the feature that the preset evaluation rule is: the overall value of the predicted yield of the other candidate reference products and the actual yield value The error value is the smallest.
- the present invention also provides an electronic device having such characteristics, the electronic device includes: a memory, a processor, the memory stores a yield prediction program in integrated circuit wafer manufacturing, When the yield prediction program in the integrated circuit wafer manufacturing is executed by the processor, the following steps are implemented:
- Obtaining candidate reference product models selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
- Prediction of alternative reference products According to the revised predicted yield models based on the current alternative reference products, the predicted yields of the alternative reference products are obtained, and the predicted yields and actual yields of each of the alternative reference products are obtained. To obtain the yield error value of each of the candidate reference products;
- Final reference product selection select the candidate reference product with the smallest yield error value as the final reference product
- New product prediction model acquisition obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
- New product yield prediction According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
- the present invention also provides a computer-readable storage medium having the feature that the yield prediction program in the integrated circuit wafer manufacturing is stored on the computer-readable storage medium, and the integrated circuit wafer When the yield prediction program in manufacturing is executed by the processor, the steps of the yield prediction method in integrated circuit wafer manufacturing are realized.
- the yield prediction method in integrated circuit wafer manufacturing involved in the present invention is based on the functional relationship between random defect density and yield in wafer manufacturing, and refers to the yield data of mature products on the production line, and establishes and Regression analysis, select the appropriate reference product, get the new product prediction model to predict the yield of the new product.
- the method in the present invention is based on the mature product database on the existing production line of the manufacturing plant as the basis of the yield prediction model. The data is true and reliable, and can reach a relatively accurate yield prediction value for new products, reflecting the actual production of the semiconductor wafer manufacturing plant Ability to establish a corresponding yield prediction model based on the product database of different factories, which is universal for semiconductor wafer manufacturing factories.
- the theoretical basis of this method is based on the correlation model function between the yield rate and the defect density in wafer manufacturing, and the modified Poisson model factor is optimized to improve the accuracy of the yield rate prediction model.
- FIG. 1 is a schematic diagram of an embodiment of an electronic device of the present invention.
- Fig. 2 is a program block diagram of an embodiment of a yield prediction program in integrated circuit wafer manufacturing of the present invention.
- FIG. 3 is a screenshot of the yield prediction program in the integrated circuit wafer manufacturing of the present invention.
- FIG. 1 is a schematic diagram of an embodiment of an electronic device of the present invention.
- the present invention provides an electronic device 1.
- FIG. 1 it is a schematic diagram of a preferred embodiment of the electronic device 1 of the present invention.
- the electronic device 1 includes a memory 11, a processor 12, a network interface 13, and a communication bus.
- the communication bus is used to realize the connection and communication between these components.
- the network interface 13 may include a standard wired interface and a wireless interface (such as a WI-FI interface).
- the memory 11 includes at least one type of readable storage medium.
- the at least one type of readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card-type memory, and the like.
- the readable storage medium may be an internal storage unit of the electronic device 1, for example, a hard disk of the electronic device 1.
- the readable storage medium may also be an external storage device of the electronic device 1, such as a plug-in hard disk or a smart memory card (Smart Media Card, SMC) equipped on the electronic device 1. , Secure Digital (SD) card, Flash Card, etc.
- SD Secure Digital
- the readable storage medium of the memory 11 is generally used to store the yield prediction program 10 and the like in the manufacture of integrated circuit wafers installed in the electronic device 1.
- the memory 11 can also be used to temporarily store data that has been output or will be output.
- the processor 12 may be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip in some embodiments, for running program codes or processing data stored in the memory 11, for example, executing integrated circuit crystals. Yield rate prediction program in circular manufacturing 10 etc.
- CPU Central Processing Unit
- microprocessor or other data processing chip in some embodiments, for running program codes or processing data stored in the memory 11, for example, executing integrated circuit crystals. Yield rate prediction program in circular manufacturing 10 etc.
- FIG. 1 only shows the electronic device 1 with the components 11-13 and the yield prediction program 10 in the manufacture of integrated circuit wafers, but it should be understood that it is not required to implement all the components shown and can be implemented instead. More or fewer components.
- the electronic device 1 may further include a user interface.
- the user interface may include a display (Display) and an input unit such as a keyboard (Keyboard).
- the optional user interface may also include a standard wired interface and a wireless interface.
- the electronic device 1 may also include a display.
- a display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device.
- the display is used to display the information processed in the electronic device and to display the visualized user interface.
- FIG. 2 shows a program module diagram of an embodiment of a yield prediction program in integrated circuit wafer manufacturing of the present invention.
- the memory 11 as a computer storage medium includes the yield prediction program 10 in integrated circuit wafer manufacturing, and the processor 12 executes the integrated circuit wafer manufacturing process stored in the memory 11.
- the yield prediction program 10 includes the following modules: alternative reference product model acquisition module 110; alternative reference product parameter acquisition module 120; alternative reference product function acquisition module 130; alternative reference product prediction module 140; final reference product selection Module 150; New product prediction model acquisition module 160; New product yield prediction module 170.
- the yield prediction program 10 in the manufacture of integrated circuit wafers may include and execute:
- the candidate reference product model obtaining module 110 selects the first number of candidate reference products in the reference product library, and obtains a linear regression equation of the full adjustment factor based on each candidate reference product according to the first preset rule.
- the reference product library may be composed of mature products already on a production line. More specifically, in some embodiments, there are 10 mature products with different product specifications (product 1, product 2...product 10) on the production line of a semiconductor integrated circuit wafer manufacturing plant, and these 10 products can be used as a reference Alternative reference products in the product library, among which the most suitable final reference product is selected. Each product has different manufacturing process, product parameters and actual yield, which can be used as the data of the yield model reference library.
- a Poisson model of yield and defect density (number of defects) is first established according to a first preset rule.
- the first preset rule is:
- Ye is the actual yield of the candidate reference product e
- ⁇ e is the average number of defects of the wafer corresponding to the candidate reference product e.
- the average wafer defect number ⁇ e is related to the product yield factor.
- the value of the full adjustment factor is affected by the yield influencing factor.
- the yield influencing factor includes the photomask layer of the reference product Number, chip area, minimum line width.
- regression analysis of the full adjustment factor can obtain the full adjustment factor linear regression equation ⁇ as:
- b is a constant term
- X 1 , X 2 , and X 3 are variable terms related to the yield influencing factors, respectively E N ratio corresponding to the variable terms reticle mask layers current product and the reference product of the number of layers N, Is the variable item corresponding to the ratio of the chip area A of the current product to the chip area A e of the reference product,
- the corresponding minimum line width W of the variable terms refer to product a minimum line width W E of the current product
- k 1 X 1 corresponds to a variable linear regression coefficients
- k 3 Is the linear regression coefficient corresponding to the variable X 3.
- FIG. 3 is a screenshot of the yield prediction program in the integrated circuit wafer manufacturing of the present invention.
- linear regression analysis is performed on the product 1, product 2...product 10, respectively, to obtain the full adjustment of each candidate reference product (product 1, product 2...product 10) Factor linear regression equation.
- the candidate reference product parameter obtaining module 120 obtains the yield influencing factors of each candidate reference product respectively.
- the yield impact factor includes the number of mask layers, chip area, and minimum line width of the reference product, as shown in Table 1 below.
- each candidate reference product (product 1, product 2...product 10) has a different number of mask layers N, area A of the wafer, and minimum line width W.
- Candidate reference product function obtaining module 130 According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate Refer to the revised predictive yield model of the product.
- the full adjustment factor linear regression equation ⁇ can also be expressed as follows:
- the full coordination factor linear function ⁇ of product 9 is:
- the modified yield prediction model of each candidate reference product can be obtained:
- the candidate reference product prediction module 140 Obtain the predicted yield of each other candidate reference product from each of the revised predicted yield models based on the current candidate reference product, and obtain the predicted yield based on each of the candidate reference products according to the second preset rule.
- the overall yield error value of the reference product is selected, and the correlation coefficient of the linear function based on the full coordination factor of each candidate reference product is obtained according to the third preset rule.
- the corresponding parameters of other products in the product library are substituted into the above-mentioned modified predictive yield model with product 9 as the reference product, so that other products corresponding to product 9 as the reference product can be obtained.
- the corresponding predicted yield and actual yield values of products in the reference library of other products are shown in Table 2 below:
- Table 2 Yield prediction statistics of other alternative reference products based on the final reference product
- the overall yield error value based on each of the candidate reference products is further obtained according to the second preset rule.
- the second preset rule includes: the overall yield error value d based on the current candidate reference product is:
- N is the total quantity of other candidate reference products
- z i is the predicted yield of other candidate reference products i
- ⁇ is the total average of the differences of all the other alternatives for each prediction refer to product yield and the actual yield.
- the correlation coefficient of the linear function of the full coordination factor based on each of the candidate reference products can also be obtained according to the third preset rule.
- the correlation is the value of the correlation coefficient R 2 of the regression analysis model corresponding to this product as the reference product. In statistics, the closer the correlation coefficient R 2 is to 1, the closer the model is to the real situation.
- the third preset rule includes: the correlation coefficient R 2 of the linear function based on the full coordination factor of each candidate reference product is
- the correlation coefficient of the linear function of the full coordination factor of each candidate reference product is obtained in turn, as shown in Table 3 above.
- the correlation coefficient of the linear function of its full coordination factor is 0.98248.
- the final reference product selection module 150 select the candidate reference product that minimizes the yield error value of the other candidate reference products as the reference product.
- the overall yield error value corresponding to product 9 in the reference product library is the smallest, which is 0.00587, and the correlation coefficient R 2 value is 0.98248, which is close to 1, indicating that the correlation is very good. Use it as the final reference product, which can be used to predict the yield of new products.
- New product prediction model acquisition 160 Obtain a new product prediction model based on the final reference product according to the full adjustment factor linear function ⁇ based on the final reference product.
- the corresponding full adjustment factor linear function ⁇ in semiconductor wafer manufacturing is:
- New product yield prediction module 170 According to the new product yield prediction model, obtain a new product yield prediction result.
- the yield prediction result of the new product can be obtained, so as to evaluate the yield of the new product on the semiconductor wafer manufacturing line. Rate level.
- the number of mask layers of the new product is 14, the minimum line width is 0.8um, and the chip area is 0.25cm2, the corresponding full adjustment factor ⁇ of the new product on the wafer manufacturing line can be obtained from the above formula
- the full adjustment factor ⁇ of the new product is 0.59554.
- an embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a yield prediction program in integrated circuit wafer manufacturing, and the yield prediction in integrated circuit wafer manufacturing The following operations are implemented when the program is executed by the processor:
- Obtaining candidate reference product models selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
- Candidate reference product prediction From each of the revised predicted yield models based on the current candidate reference product, the predicted yield of each other candidate reference product is obtained, and the predicted yield of each other candidate reference product is obtained according to the second preset rule.
- the overall yield error value of the product is obtained according to the third preset rule based on the correlation coefficient of the linear function of the full coordination factor of each candidate reference product;
- Final reference product selection select the candidate reference product with the smallest overall yield error value or the linear function correlation coefficient of the full coordination factor closest to 1 as the final reference product;
- New product prediction model acquisition obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
- New product yield prediction According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
- the specific implementation of the computer-readable storage medium of the present invention is substantially the same as the specific implementation of the yield prediction method in the fabrication of integrated circuit wafers, and will not be repeated here.
- the yield prediction method in integrated circuit wafer manufacturing involved in this embodiment based on the functional relationship between random defect density and yield in wafer manufacturing, refer to the yield data of mature products on the production line, and use the data model Establish and regression analysis, select appropriate reference products, and obtain new product prediction models to predict the yield of new products.
- the method in the present invention is based on the mature product database on the existing production line of the manufacturing plant as the basis of the yield prediction model. The data is true and reliable, and can reach a relatively accurate yield prediction value for new products, reflecting the actual production of the semiconductor wafer manufacturing plant Ability to establish a corresponding yield prediction model based on the product database of different factories, which is universal for semiconductor wafer manufacturing factories.
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Abstract
A yield prediction method in integrated circuit wafer manufacturing, comprising the following steps: acquiring a candidate reference product model; acquiring candidate reference product parameters; acquiring a candidate reference product function; predicting a candidate reference product; selecting a final reference product; acquiring a new product prediction model; and predicting new product yield. The described method is based on the functional relationship between random defect density and yield in wafer manufacturing; by referring to yield data of existing mature products on a production line, and by using data model establishment and regression analysis, a relatively accurate prediction value of new product yield can be attained, thus providing semiconductor integrated circuit wafer manufacturing with a method for predicting the yield of a new product.
Description
本发明涉及一种集成电路晶圆制造中的良率预测方法。The invention relates to a method for predicting yield in integrated circuit wafer manufacturing.
在半导体集成电路晶圆制造过程中,特别是新产品开发过程中,新产品的良率对于初始的成本预算及产出价值有着关键的影响,而半导体集成电路晶圆制造涉及制造程序复杂,制造工艺繁多,产品种类众多,其中关于新产品良率预测有很大程度的不确定性和主观经验性因素,由于不能准确的预测新产品的投产良率,从而使得在生产制造前期决策时间延长,增加生产的时间成本及其他相关成本,并导致在决策新产品研发投产前无法准确估计相应的产出价值。In the semiconductor integrated circuit wafer manufacturing process, especially in the new product development process, the yield of new products has a key impact on the initial cost budget and output value. The semiconductor integrated circuit wafer manufacturing involves complex manufacturing procedures and manufacturing There are many processes and many types of products. Among them, there is a large degree of uncertainty and subjective empirical factors in the prediction of new product yield. Due to the inability to accurately predict the production yield of new products, the decision-making time in the early stage of production is prolonged. Increasing the time cost of production and other related costs, and leading to the inability to accurately estimate the corresponding output value before the decision to develop new products and put into production.
发明内容Summary of the invention
本发明是为了解决上述问题而进行的,提供了一种集成电路晶圆制造中的良率预测方法,基于晶圆制造中的随机缺陷密度与良率的函数关系,参考生产线上已有成熟产品的良率数据,通过数据模型建立及回归分析,能达到一个比较精确的新产品良率预测值,给半导体集成电路晶圆制造提供一种新产品良率预测的方法。The present invention is carried out in order to solve the above-mentioned problems. It provides a yield prediction method in integrated circuit wafer manufacturing. Based on the functional relationship between random defect density and yield in wafer manufacturing, refer to the mature products on the production line. Through the establishment of data model and regression analysis, the yield data of new products can reach a relatively accurate prediction value of the yield of new products, which provides a method of predicting the yield of new products for semiconductor integrated circuit wafer manufacturing.
为实现上述目的,本发明提出一种集成电路晶圆制造中的良率预测方法,具有这样的特征,包括以下步骤:In order to achieve the above objective, the present invention proposes a yield prediction method in integrated circuit wafer manufacturing, which has such characteristics and includes the following steps:
备选参考产品模型获取:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程;Obtaining candidate reference product models: selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
备选参考产品参数获取:分别获取各所述备选参考产品的各良率影响因子;Obtaining the parameters of the candidate reference products: respectively obtaining the yield impact factors of each of the candidate reference products;
备选参考产品函数获取:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型;Obtaining alternative reference product functions: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate reference product Modified prediction yield model;
备选参考产品预测:由各所述基于当前备选参考产品的修正预测良率模型,得到各其他备选参考产品的预测良率,根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值,根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数;Candidate reference product prediction: From each of the revised predicted yield models based on the current candidate reference product, the predicted yield of each other candidate reference product is obtained, and the predicted yield of each other candidate reference product is obtained according to the second preset rule. The overall yield error value of the product is obtained according to the third preset rule based on the correlation coefficient of the linear function of the full coordination factor of each candidate reference product;
最终参考产品选择:选取所述整体良率误差值最小或全协调因子线性函数相关系数最接近1的备选参考产品作为最终参考产品;Final reference product selection: select the candidate reference product with the smallest overall yield error value or the linear function correlation coefficient of the full coordination factor closest to 1 as the final reference product;
新产品预测模型获取:根据基于所述最终参考产品的所述全调整因子线性函数,得到基于所述最终参考产品的新产品预测模型;New product prediction model acquisition: obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
新产品良率预测:根据所述新产品良率预测模型以及所述新产品的良率影响因子,得到新产品良率预测结果。New product yield prediction: According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述第一预设规则为:In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the first preset rule is:
其中,Y
e为所述备选参考产品e的实际良率,λ
e为对应所述备选参考产品e的晶圆平均缺陷数。
Wherein, Ye is the actual yield of the candidate reference product e, and λ e is the average number of defects of the wafer corresponding to the candidate reference product e.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述良率影响因子包含所述参考产品的光罩层数、晶片面积、最小线宽。In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the yield impact factor includes the number of mask layers, chip area, and minimum line width of the reference product.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述全调整因子线性回归方程σ为:In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the full adjustment factor linear regression equation σ is:
σ=-ln Y/λ
e=f(X
1,X
2,X
3,···)=b+k
1X
1+k
2X
2+k
3X
3+···
σ=-ln Y/λ e = f(X 1 ,X 2 ,X 3 ,···)=b+k 1 X 1 +k 2 X 2 +k 3 X 3 +···
其中,b为常数项,X
1、X
2、X
3分别为与各良率影响因子相关的变量项
为当前产品的光罩层数N与参考产品的光罩层数N
e之比对应的变量项,
为当前产品的晶片面积A与参考产品的晶片面积A
e之比对应的变量项,
为参考产品的最小线宽W
e与当前产品的最小线宽W之比对应的变量项,k
1为变量X
1对应的线性回归系数,k
2为变量X
2对应的线性回归系数,k
3为变量X
3对应的线性回归系数。
Among them, b is a constant term, and X 1 , X 2 , and X 3 are variable terms related to the yield influencing factors, respectively E N ratio corresponding to the variable terms reticle mask layers current product and the reference product of the number of layers N, Is the variable item corresponding to the ratio of the chip area A of the current product to the chip area A e of the reference product, Than the corresponding minimum line width W of the variable terms refer to product a minimum line width W E of the current product, k 1 X 1 corresponds to a variable linear regression coefficients, k 2 X 2 linear regression coefficients corresponding to the variable, k 3 Is the linear regression coefficient corresponding to the variable X 3.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述全协调因子线性函数为:In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the linear function of the full coordination factor is:
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述修正良率预测模型为:In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the modified yield prediction model is:
其中,
为产品i基于所述备选参考产品e的预测良率,σ
i为产品i基于所述备选参考产品e的全调整因子,Y
e为所述备选参考产品e的实际生产良率。
in, Is the predicted yield of product i based on the candidate reference product e, σ i is the full adjustment factor of product i based on the candidate reference product e, and Ye is the actual production yield of the candidate reference product e.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述第二预设规则为:基于当前备选参考产品时的整体良率误差值d为In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the second preset rule is: the overall yield error value d based on the current candidate reference product is
其中,N为所述他备选参考产品总数量,z
i为其他备选参考产品i的预测良率
与实际良率Y
i差值,μ为所有所述其他备选参考产品的各预测良率与实际良率的差值的总平均值。
Among them, N is the total quantity of other candidate reference products, z i is the predicted yield of other candidate reference products i The difference between the actual yield Y i, μ is the total average of the differences of all the other alternatives for each prediction refer to product yield and the actual yield.
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述第三预设规则包括:基于各所述备选参考产品的全协调因子线性函数相关系数R为In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the third preset rule includes: a linear function correlation coefficient R based on the full coordination factor of each candidate reference product for
其中,所有所述其他备选参考产品的实际良率的平均值
Among them, the average value of the actual yields of all the other candidate reference products
另外,本发明提供的集成电路晶圆制造中的良率预测方法,还具有这样的特征,所述预设评估规则为:所述其他备选参考产品的预测良率与实际良率值的整体误差值最小。In addition, the yield prediction method in integrated circuit wafer manufacturing provided by the present invention also has the feature that the preset evaluation rule is: the overall value of the predicted yield of the other candidate reference products and the actual yield value The error value is the smallest.
此外,为实现上述目的,本发明还提供了一种电子装置,具有这样的 特征,该电子装置包括:存储器、处理器,所述存储器上存储有集成电路晶圆制造中的良率预测程序,所述集成电路晶圆制造中的良率预测程序被所述处理器执行时实现以下步骤:In addition, in order to achieve the above-mentioned object, the present invention also provides an electronic device having such characteristics, the electronic device includes: a memory, a processor, the memory stores a yield prediction program in integrated circuit wafer manufacturing, When the yield prediction program in the integrated circuit wafer manufacturing is executed by the processor, the following steps are implemented:
备选参考产品模型获取:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程;Obtaining candidate reference product models: selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
备选参考产品参数获取:分别获取各所述备选参考产品的各良率影响因子;Obtaining the parameters of the candidate reference products: respectively obtaining the yield impact factors of each of the candidate reference products;
备选参考产品函数获取:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型;Obtaining alternative reference product functions: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate reference product Modified prediction yield model;
备选参考产品预测:根据各所述基于当前备选参考产品的修正预测良率模型,分别得到各所述备选参考产品的预测良率,并根据各所述预测良率与各实际良率的差值得到各所述备选参考产品的良率误差值;Prediction of alternative reference products: According to the revised predicted yield models based on the current alternative reference products, the predicted yields of the alternative reference products are obtained, and the predicted yields and actual yields of each of the alternative reference products are obtained. To obtain the yield error value of each of the candidate reference products;
最终参考产品选择:选取所述良率误差值最小的备选参考产品作为最终参考产品;Final reference product selection: select the candidate reference product with the smallest yield error value as the final reference product;
新产品预测模型获取:根据基于所述最终参考产品的所述全调整因子线性函数,得到基于所述最终参考产品的新产品预测模型;New product prediction model acquisition: obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
新产品良率预测:根据所述新产品良率预测模型以及所述新产品的良率影响因子,得到新产品良率预测结果。New product yield prediction: According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
另外,本发明还提供了一种计算机可读存储介质,具有这样的特征,所述计算机可读存储介质上存储有所述集成电路晶圆制造中的良率预测程序,所述集成电路晶圆制造中的良率预测程序被处理器执行时实现上述集 成电路晶圆制造中的良率预测方法的步骤。In addition, the present invention also provides a computer-readable storage medium having the feature that the yield prediction program in the integrated circuit wafer manufacturing is stored on the computer-readable storage medium, and the integrated circuit wafer When the yield prediction program in manufacturing is executed by the processor, the steps of the yield prediction method in integrated circuit wafer manufacturing are realized.
发明作用和效果Invention effect and effect
本发明所涉及的集成电路晶圆制造中的良率预测方法,基于晶圆制造中的随机缺陷密度与良率的函数关系,参考生产线上已有成熟产品的良率数据,通过数据模型建立及回归分析,选择合适的参考产品,得到新产品预测模型,以进行新产品良率预测。本发明中的方法依据制造工厂现有生产线上的成熟产品数据库作为良率预测模型的基础,数据真实可靠,能达到比较精确的新产品良率预测值,反应该半导体晶圆制造工厂的实际生产能力,可以根据不同工厂的产品数据库建立对应的良率预测模型,对半导体晶圆制造工厂有普适性。The yield prediction method in integrated circuit wafer manufacturing involved in the present invention is based on the functional relationship between random defect density and yield in wafer manufacturing, and refers to the yield data of mature products on the production line, and establishes and Regression analysis, select the appropriate reference product, get the new product prediction model to predict the yield of the new product. The method in the present invention is based on the mature product database on the existing production line of the manufacturing plant as the basis of the yield prediction model. The data is true and reliable, and can reach a relatively accurate yield prediction value for new products, reflecting the actual production of the semiconductor wafer manufacturing plant Ability to establish a corresponding yield prediction model based on the product database of different factories, which is universal for semiconductor wafer manufacturing factories.
本方法理论基础依据良率与晶圆制造中缺陷密度的相关性模型函数,经过修正泊松模型因子优化,提高了良率预测模型的准确性。The theoretical basis of this method is based on the correlation model function between the yield rate and the defect density in wafer manufacturing, and the modified Poisson model factor is optimized to improve the accuracy of the yield rate prediction model.
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图示出的结构获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained based on the structure shown in these drawings.
图1是本发明电子装置一实施例的示意图。FIG. 1 is a schematic diagram of an embodiment of an electronic device of the present invention.
图2是本发明的集成电路晶圆制造中的良率预测程序一实施例的程序 模块图。Fig. 2 is a program block diagram of an embodiment of a yield prediction program in integrated circuit wafer manufacturing of the present invention.
图3是是本发明的集成电路晶圆制造中的良率预测程序截图。FIG. 3 is a screenshot of the yield prediction program in the integrated circuit wafer manufacturing of the present invention.
本发明目的的实现、功能特点及优点将结合实施例,参照附图做进一步说明。The realization of the objectives, functional characteristics and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.
以下参照附图及实施例对本发明所涉及的一种集成电路晶圆制造中的良率预测方法、装置和存储设备作详细的描述。以下结合附图对本发明的原理和特征进行描述,所举实例只用于解释本发明,并非用于限定本发明的范围。Hereinafter, a method, device, and storage device for yield prediction in integrated circuit wafer manufacturing involved in the present invention will be described in detail with reference to the accompanying drawings and embodiments. The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples cited are only used to explain the present invention, and are not used to limit the scope of the present invention.
图1是本发明电子装置一实施例的示意图。FIG. 1 is a schematic diagram of an embodiment of an electronic device of the present invention.
本发明提供一种电子装置1。参照图1所示,为本发明电子装置1较佳实施例的示意图。The present invention provides an electronic device 1. Referring to FIG. 1, it is a schematic diagram of a preferred embodiment of the electronic device 1 of the present invention.
在本实施例中,该电子装置1包括存储器11、处理器12,网络接口13及通信总线。其中,通信总线用于实现这些组件之间的连接通信。In this embodiment, the electronic device 1 includes a memory 11, a processor 12, a network interface 13, and a communication bus. Among them, the communication bus is used to realize the connection and communication between these components.
网络接口13可以包括标准的有线接口、无线接口(如WI-FI接口)。The network interface 13 may include a standard wired interface and a wireless interface (such as a WI-FI interface).
存储器11包括至少一种类型的可读存储介质。所述至少一种类型的可读存储介质可为如闪存、硬盘、多媒体卡、卡型存储器等的非易失性存储介质。在一些实施例中,所述可读存储介质可以是所述电子装置1的内部存储单元,例如该电子装置1的硬盘。在另一些实施例中,所述可读存储介质也可以是所述电子装置1的外部存储设备,例如所述电子装置1上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。The memory 11 includes at least one type of readable storage medium. The at least one type of readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card-type memory, and the like. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 1, for example, a hard disk of the electronic device 1. In other embodiments, the readable storage medium may also be an external storage device of the electronic device 1, such as a plug-in hard disk or a smart memory card (Smart Media Card, SMC) equipped on the electronic device 1. , Secure Digital (SD) card, Flash Card, etc.
在本实施例中,所述存储器11的可读存储介质通常用于存储安装于所述电子装置1的集成电路晶圆制造中的良率预测程序10等。所述存储器11还可以用于暂时地存储已经输出或者将要输出的数据。In this embodiment, the readable storage medium of the memory 11 is generally used to store the yield prediction program 10 and the like in the manufacture of integrated circuit wafers installed in the electronic device 1. The memory 11 can also be used to temporarily store data that has been output or will be output.
处理器12在一些实施例中可以是一中央处理器(Central Processing Unit,CPU),微处理器或其他数据处理芯片,用于运行存储器11中存储的程序代码或处理数据,例如执行集成电路晶圆制造中的良率预测程序10等。The processor 12 may be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip in some embodiments, for running program codes or processing data stored in the memory 11, for example, executing integrated circuit crystals. Yield rate prediction program in circular manufacturing 10 etc.
图1仅示出了具有组件11-13以及集成电路晶圆制造中的良率预测程序10的电子装置1,但是应理解的是,并不要求实施所有示出的组件,可以替代的实施更多或者更少的组件。FIG. 1 only shows the electronic device 1 with the components 11-13 and the yield prediction program 10 in the manufacture of integrated circuit wafers, but it should be understood that it is not required to implement all the components shown and can be implemented instead. More or fewer components.
可选的,该电子装置1还可以包括用户接口,用户接口可以包括显示器(Display)、输入单元比如键盘(Keyboard),可选的用户接口还可以包括标准的有线接口、无线接口。Optionally, the electronic device 1 may further include a user interface. The user interface may include a display (Display) and an input unit such as a keyboard (Keyboard). The optional user interface may also include a standard wired interface and a wireless interface.
可选地,该电子装置1还可以包括显示器,在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及有机发光二极管(Organic Light-Emitting Diode,OLED)触摸器等。显示器用于显示在电子装置中处理的信息以及用于显示可视化的用户界面。Optionally, the electronic device 1 may also include a display. In some embodiments, it may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device. The display is used to display the information processed in the electronic device and to display the visualized user interface.
图2示本发明的集成电路晶圆制造中的良率预测程序一实施例的程序模块图。FIG. 2 shows a program module diagram of an embodiment of a yield prediction program in integrated circuit wafer manufacturing of the present invention.
在图1所示的装置实施例中,作为一种计算机存储介质的存储器11中包括集成电路晶圆制造中的良率预测程序10,处理器12执行存储器11中 存储的集成电路晶圆制造中的良率预测程序10时包括以下模块:备选参考产品模型获取模块110;备选参考产品参数获取模块120;备选参考产品函数获取模块130;备选参考产品预测模块140;最终参考产品选择模块150;新产品预测模型获取模块160;新产品良率预测模块170。In the device embodiment shown in FIG. 1, the memory 11 as a computer storage medium includes the yield prediction program 10 in integrated circuit wafer manufacturing, and the processor 12 executes the integrated circuit wafer manufacturing process stored in the memory 11. The yield prediction program 10 includes the following modules: alternative reference product model acquisition module 110; alternative reference product parameter acquisition module 120; alternative reference product function acquisition module 130; alternative reference product prediction module 140; final reference product selection Module 150; New product prediction model acquisition module 160; New product yield prediction module 170.
如图2所示,在本实施例中,集成电路晶圆制造中的良率预测程序10可以包括并执行:As shown in FIG. 2, in this embodiment, the yield prediction program 10 in the manufacture of integrated circuit wafers may include and execute:
备选参考产品模型获取模块110:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程。The candidate reference product model obtaining module 110: selects the first number of candidate reference products in the reference product library, and obtains a linear regression equation of the full adjustment factor based on each candidate reference product according to the first preset rule.
在本发明中,所述的参考产品库可以是由一条生产线上已有的成熟产品组成。更具体而言,在一些实施例中,某半导体集成电路晶圆制造工厂的生产线上有10种成熟的不同产品规格产品(产品1,产品2…产品10),可将该10种产品作为参考产品库中的备选参考产品,在其中选出最合适的最终参考产品。每种产品具有不同的制程、产品参数与实际良率,可以作为良率模型参考库的数据。In the present invention, the reference product library may be composed of mature products already on a production line. More specifically, in some embodiments, there are 10 mature products with different product specifications (product 1, product 2...product 10) on the production line of a semiconductor integrated circuit wafer manufacturing plant, and these 10 products can be used as a reference Alternative reference products in the product library, among which the most suitable final reference product is selected. Each product has different manufacturing process, product parameters and actual yield, which can be used as the data of the yield model reference library.
更具体而言,本发明提供的集成电路晶圆制造中的良率预测程序中,首先根据第一预设规则建立良率与缺陷密度(缺陷数)的泊松模型。More specifically, in the yield prediction program in integrated circuit wafer manufacturing provided by the present invention, a Poisson model of yield and defect density (number of defects) is first established according to a first preset rule.
所述第一预设规则为:The first preset rule is:
其中,Y
e为所述备选参考产品e的实际良率,λ
e为对应所述备选参考产品e的晶圆平均缺陷数。
Wherein, Ye is the actual yield of the candidate reference product e, and λ e is the average number of defects of the wafer corresponding to the candidate reference product e.
所述晶圆平均缺陷数λe与产品的良率影响因子有关。The average wafer defect number λe is related to the product yield factor.
因此,修正的泊松模型的参数即全调整因子:Therefore, the parameters of the modified Poisson model are the full adjustment factors:
σ=-lnY/λ
e
σ=-lnY/λ e
另外,所述全调整因子的数值受良率影响因子的影响,在本发明提供的集成电路晶圆制造中的良率预测方法中,所述良率影响因子包含所述参考产品的光罩层数、晶片面积、最小线宽。In addition, the value of the full adjustment factor is affected by the yield influencing factor. In the yield prediction method in integrated circuit wafer manufacturing provided by the present invention, the yield influencing factor includes the photomask layer of the reference product Number, chip area, minimum line width.
进一步,根据所述良率与缺陷密度(缺陷数)的泊松模型,对所述全调整因子进行回归分析可得到所述全调整因子线性回归方程σ为:Furthermore, according to the Poisson model of yield and defect density (number of defects), regression analysis of the full adjustment factor can obtain the full adjustment factor linear regression equation σ as:
σ=-ln Y/λ
e=f(X
1,X
2,X
3,···)=b+k
1X
1+k
2X
2+k
3X
3+···
σ=-ln Y/λ e = f(X 1 ,X 2 ,X 3 ,···)=b+k 1 X 1 +k 2 X 2 +k 3 X 3 +···
其中,b为常数项,X
1、X
2、X
3分别为与各良率影响因子相关的变量项
为当前产品的光罩层数N与参考产品的光罩层数N
e之比对应的变量项,
为当前产品的晶片面积A与参考产品的晶片面积A
e之比对应的变量项,
为参考产品的最小线宽W
e与当前产品的最小线宽W之比对应的变量项,k
1为变量X
1对应的线性回归系数,k
2为变量X
2对应的线性回归系数,k
3为变量X
3对应的线性回归系数。
Among them, b is a constant term, and X 1 , X 2 , and X 3 are variable terms related to the yield influencing factors, respectively E N ratio corresponding to the variable terms reticle mask layers current product and the reference product of the number of layers N, Is the variable item corresponding to the ratio of the chip area A of the current product to the chip area A e of the reference product, Than the corresponding minimum line width W of the variable terms refer to product a minimum line width W E of the current product, k 1 X 1 corresponds to a variable linear regression coefficients, k 2 X 2 linear regression coefficients corresponding to the variable, k 3 Is the linear regression coefficient corresponding to the variable X 3.
根据所述全调整因子线性回归模型σ可分别得到基于各备选参考产品的全调整因子线性回归方程。According to the full adjustment factor linear regression model σ, a full adjustment factor linear regression equation based on each candidate reference product can be obtained respectively.
图3是是本发明的集成电路晶圆制造中的良率预测程序截图。FIG. 3 is a screenshot of the yield prediction program in the integrated circuit wafer manufacturing of the present invention.
如图3所示,在一些实施例中,对所述产品1,产品2…产品10分别 进行线性回归分析,得到各备选参考产品(产品1,产品2...产品10)的全调整因子线性回归方程。As shown in Figure 3, in some embodiments, linear regression analysis is performed on the product 1, product 2...product 10, respectively, to obtain the full adjustment of each candidate reference product (product 1, product 2...product 10) Factor linear regression equation.
备选参考产品参数获取模块120:分别获取各所述备选参考产品的各良率影响因子。The candidate reference product parameter obtaining module 120: obtains the yield influencing factors of each candidate reference product respectively.
在本发明中的集成电路晶圆制造中的良率预测程序中,所述良率影响因子包含所述参考产品的光罩层数、晶片面积、最小线宽,如下表1所示,在一些实施例中,每个备选参考产品(产品1,产品2...产品10)具有不同的光罩层数N、晶片的面积A,以及最小线宽W。In the yield prediction program in integrated circuit wafer manufacturing of the present invention, the yield impact factor includes the number of mask layers, chip area, and minimum line width of the reference product, as shown in Table 1 below. In the embodiment, each candidate reference product (product 1, product 2...product 10) has a different number of mask layers N, area A of the wafer, and minimum line width W.
表1参考产品库中各备选参考产品的良率影响因子统计Table 1 Statistics on the yield impact factors of each candidate reference product in the reference product library
产品product | 光罩层数Number of mask layers | 晶片面积cm2Chip area cm2 | 最小线宽umMinimum line width um | 平均良率Average yield |
11 | 1111 | 0.15330.1533 | 1.01.0 | 95.6%95.6% |
22 | 1010 | 0.18790.1879 | 0.80.8 | 90.2%90.2% |
33 | 1212 | 0.20530.2053 | 0.80.8 | 87.4%87.4% |
44 | 1414 | 0.08940.0894 | 0.60.6 | 82.5%82.5% |
55 | 1010 | 0.14690.1469 | 0.70.7 | 90.8%90.8% |
66 | 1313 | 0.13760.1376 | 1.01.0 | 93.5%93.5% |
77 | 1212 | 0.10910.1091 | 0.60.6 | 84.2%84.2% |
88 | 1111 | 0.16950.1695 | 0.80.8 | 91.4%91.4% |
99 | 1212 | 0.14480.1448 | 0.60.6 | 87.4%87.4% |
1010 | 1111 | 0.19860.1986 | 0.70.7 | 85.9%85.9% |
备选参考产品函数获取模块130:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型。Candidate reference product function obtaining module 130: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate Refer to the revised predictive yield model of the product.
由于晶圆制造的产品良率与该产品的光罩层数N、晶片面积A、最小线宽W等产品制程的参数相关,因此所述全调整因子线性回归方程σ也可表示 如下:Since the product yield rate of wafer manufacturing is related to the product process parameters such as the number of mask layers N, the chip area A, and the minimum line width W of the product, the full adjustment factor linear regression equation σ can also be expressed as follows:
因而,对于参考产品库中的每一种备选参考产品,当代入各备选参考产品的良率影响因子后,均可以得到对应的修正泊松模型全协调因子线性函数。例如,在一些实施例中,产品9的全协调因子线性函数σ为:Therefore, for each candidate reference product in the reference product library, after entering the yield impact factor of each candidate reference product, the corresponding modified Poisson model full coordination factor linear function can be obtained. For example, in some embodiments, the full coordination factor linear function σ of product 9 is:
另外,本发明提供的集成电路晶圆制造中的良率预测方法中,根据所述全调整因子线性回归方程,可得到各备选参考产品修正良率预测模型:In addition, in the yield prediction method in integrated circuit wafer manufacturing provided by the present invention, according to the linear regression equation of the full adjustment factor, the modified yield prediction model of each candidate reference product can be obtained:
其中,
为产品i基于所述备选参考产品e的预测良率,σ
i为产品i基于所述备选参考产品e的全调整因子,Y
e为所述备选参考产品e的实际生产良率。
in, Is the predicted yield of product i based on the candidate reference product e, σ i is the full adjustment factor of product i based on the candidate reference product e, and Ye is the actual production yield of the candidate reference product e.
备选参考产品预测模块140:由各所述基于当前备选参考产品的修正预测良率模型,得到各其他备选参考产品的预测良率,根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值,根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数。The candidate reference product prediction module 140: Obtain the predicted yield of each other candidate reference product from each of the revised predicted yield models based on the current candidate reference product, and obtain the predicted yield based on each of the candidate reference products according to the second preset rule. The overall yield error value of the reference product is selected, and the correlation coefficient of the linear function based on the full coordination factor of each candidate reference product is obtained according to the third preset rule.
如上所述,例如,将产品9的全调整因子线性函数σ代入各备选参考产品修正良率预测模型,得到产品9所对应的修正预测良率模型:As mentioned above, for example, the full adjustment factor linear function σ of product 9 is substituted into the modified yield prediction model of each candidate reference product to obtain the modified prediction yield model corresponding to product 9:
在一些实施例中,基于所述修正预测良率模型,将产品库其他产品对 应参数代入上述以产品9为参考产品的修正预测良率模型,即可求得以产品9为参考产品对应的的其他产品的预测良率。对应的其他产品参考库中产品的预测良率与实际良率数值如下表2所示:In some embodiments, based on the modified predictive yield model, the corresponding parameters of other products in the product library are substituted into the above-mentioned modified predictive yield model with product 9 as the reference product, so that other products corresponding to product 9 as the reference product can be obtained. The predicted yield of the product. The corresponding predicted yield and actual yield values of products in the reference library of other products are shown in Table 2 below:
表2基于最终参考产品的其他备选参考产品的良率预测统计Table 2 Yield prediction statistics of other alternative reference products based on the final reference product
产品product | 11 | 22 | 33 | 44 | 55 | 66 | 77 | 88 | 1010 |
平均良率Average yield | 95.6%95.6% | 90.2%90.2% | 87.4%87.4% | 82.5%82.5% | 90.8%90.8% | 93.5%93.5% | 84.2%84.2% | 91.4%91.4% | 85.9%85.9% |
预测良率Predicted yield | 96.0%96.0% | 91.2%91.2% | 87.2%87.2% | 82.5%82.5% | 90.0%90.0% | 93.4%93.4% | 84.6%84.6% | 90.5%90.5% | 86.0%86.0% |
如上表所示,进一步根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值。所述第二预设规则包括:基于当前备选参考产品时的整体良率误差值d为:As shown in the above table, the overall yield error value based on each of the candidate reference products is further obtained according to the second preset rule. The second preset rule includes: the overall yield error value d based on the current candidate reference product is:
其中,N为所述他备选参考产品总数量,z
i为其他备选参考产品i的预测良率
与实际良率Y
i差值,μ为所有所述其他备选参考产品的各预测良率与实际良率的差值的总平均值。
Among them, N is the total quantity of other candidate reference products, z i is the predicted yield of other candidate reference products i The difference between the actual yield Y i, μ is the total average of the differences of all the other alternatives for each prediction refer to product yield and the actual yield.
依次求得各备选参考产品的整体良率误差值。如下表3所示。以产品9为例,将各其他备选参考产品的各平均良率和预测良率代入上式,得到整体良率误差值为0.00587。Obtain the overall yield error value of each candidate reference product in turn. As shown in Table 3 below. Taking product 9 as an example, substituting the average yield and predicted yield of each other candidate reference product into the above formula, the overall yield error value is 0.00587.
表3各备选参考产品的σ函数回归分析和整体良率误差值统计Table 3 σ function regression analysis and overall yield error statistics of each candidate reference product
参考产品Reference product | σ函数相关性σ function correlation | 整体良率误差值Overall yield error value |
11 | 0.831160.83116 | 0.014570.01457 |
22 | 0.894310.89431 | 0.385830.38583 |
33 | 0.883190.88319 | 0.014480.01448 |
44 | 0.860240.86024 | 0.013270.01327 |
55 | 0.880830.88083 | 0.014490.01449 |
66 | 0.867440.86744 | 0.014360.01436 |
77 | 0.826920.82692 | 0.016380.01638 |
88 | 0.880240.88024 | 0.014380.01438 |
99 | 0.982480.98248 | 0.005870.00587 |
1010 | 0.885490.88549 | 0.014050.01405 |
另外,还可根据根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数。相关性为以该种产品为参考产品对应的回归分析模型的相关系数R
2值,统计学中相关系数R
2越接近于1说明该模型越接近于真实情况。
In addition, the correlation coefficient of the linear function of the full coordination factor based on each of the candidate reference products can also be obtained according to the third preset rule. The correlation is the value of the correlation coefficient R 2 of the regression analysis model corresponding to this product as the reference product. In statistics, the closer the correlation coefficient R 2 is to 1, the closer the model is to the real situation.
所述第三预设规则包括:基于各所述备选参考产品的全协调因子线性函数相关系数R
2为
The third preset rule includes: the correlation coefficient R 2 of the linear function based on the full coordination factor of each candidate reference product is
其中,所有所述其他备选参考产品的实际良率的平均值
Among them, the average value of the actual yields of all the other candidate reference products
由上述步骤依次求得各备选参考产品的全协调因子线性函数相关系数,如上表3所示。以产品9为例,将各其他备选参考产品的各平均良率 和预测良率代入上式,得到其全协调因子线性函数相关系数0.98248。According to the above steps, the correlation coefficient of the linear function of the full coordination factor of each candidate reference product is obtained in turn, as shown in Table 3 above. Taking product 9 as an example, substituting the average yield and predicted yield of each other candidate reference product into the above formula, the correlation coefficient of the linear function of its full coordination factor is 0.98248.
最终参考产品选择模块150:选取使所述其他备选参考产品良率误差值最小的备选参考产品作为参考产品。The final reference product selection module 150: select the candidate reference product that minimizes the yield error value of the other candidate reference products as the reference product.
在一些实施例中,由表3所得,该参考产品库内产品9对应的良率整体误差值最小,为0.00587,并且相关系数R
2值为0.98248,接近于1,说明相关性很好,可将其作为最终参考产品,可以用于新产品良率预测。
In some embodiments, from Table 3, the overall yield error value corresponding to product 9 in the reference product library is the smallest, which is 0.00587, and the correlation coefficient R 2 value is 0.98248, which is close to 1, indicating that the correlation is very good. Use it as the final reference product, which can be used to predict the yield of new products.
新产品预测模型获取160:根据基于所述最终参考产品的所述全调整因子线性函数σ,得到基于所述最终参考产品的新产品预测模型。New product prediction model acquisition 160: Obtain a new product prediction model based on the final reference product according to the full adjustment factor linear function σ based on the final reference product.
在一些实施例中,当选取第9种产品为参考产品时,对应的该半导体晶圆制造中的全调整因子线性函数σ即为:In some embodiments, when the ninth product is selected as the reference product, the corresponding full adjustment factor linear function σ in semiconductor wafer manufacturing is:
则所述新产品预测模型为:Then the new product prediction model is:
新产品良率预测模块170:根据所述新产品良率预测模型,得到新产品良率预测结果。New product yield prediction module 170: According to the new product yield prediction model, obtain a new product yield prediction result.
在一些实施例中,根据上述新产品良率预测模型以及新产品的良率影响因子,可以得到新产品良率预测结果,从而评估该新产品在该半导体晶圆制造产线上能达到的良率水平。In some embodiments, according to the above-mentioned new product yield prediction model and the yield impact factor of the new product, the yield prediction result of the new product can be obtained, so as to evaluate the yield of the new product on the semiconductor wafer manufacturing line. Rate level.
具体而言,所述新产品的光罩层数为14,最小线宽0.8um,晶片面积0.25cm2,则该新产品在该晶圆制造生产线上的对应全调整因子σ,可由上面公式得出新产品的全调整因子σ=0.59554。新产品对应的预测良率值:Specifically, the number of mask layers of the new product is 14, the minimum line width is 0.8um, and the chip area is 0.25cm2, the corresponding full adjustment factor σ of the new product on the wafer manufacturing line can be obtained from the above formula The full adjustment factor σ of the new product is 0.59554. The predicted yield value corresponding to the new product:
此外,本发明实施例还提出一种计算机可读存储介质,所述计算机可读存储介质上存储有集成电路晶圆制造中的良率预测程序,所述集成电路晶圆制造中的良率预测程序被处理器执行时实现如下操作:In addition, an embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a yield prediction program in integrated circuit wafer manufacturing, and the yield prediction in integrated circuit wafer manufacturing The following operations are implemented when the program is executed by the processor:
备选参考产品模型获取:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程;Obtaining candidate reference product models: selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;
备选参考产品参数获取:分别获取各所述备选参考产品的各良率影响因子;Obtaining the parameters of the candidate reference products: respectively obtaining the yield influencing factors of each of the candidate reference products;
备选参考产品函数获取:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型;Obtaining alternative reference product functions: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate reference product Modified prediction yield model;
备选参考产品预测:由各所述基于当前备选参考产品的修正预测良率模型,得到各其他备选参考产品的预测良率,根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值,根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数;Candidate reference product prediction: From each of the revised predicted yield models based on the current candidate reference product, the predicted yield of each other candidate reference product is obtained, and the predicted yield of each other candidate reference product is obtained according to the second preset rule. The overall yield error value of the product is obtained according to the third preset rule based on the correlation coefficient of the linear function of the full coordination factor of each candidate reference product;
最终参考产品选择:选取所述整体良率误差值最小或全协调因子线性函数相关系数最接近1的备选参考产品作为最终参考产品;Final reference product selection: select the candidate reference product with the smallest overall yield error value or the linear function correlation coefficient of the full coordination factor closest to 1 as the final reference product;
新产品预测模型获取:根据基于所述最终参考产品的所述全调整因子线性函数,得到基于所述最终参考产品的新产品预测模型;New product prediction model acquisition: obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;
新产品良率预测:根据所述新产品良率预测模型以及所述新产品的良率影响因子,得到新产品良率预测结果。New product yield prediction: According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
本发明之计算机可读存储介质的具体实施方式与上述集成电路晶圆制 造中的良率预测方法的具体实施方式大致相同,在此不再赘述。The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the specific implementation of the yield prediction method in the fabrication of integrated circuit wafers, and will not be repeated here.
实施例的作用与效果The function and effect of the embodiment
根据本实施例所涉及的集成电路晶圆制造中的良率预测方法,基于晶圆制造中的随机缺陷密度与良率的函数关系,参考生产线上已有成熟产品的良率数据,通过数据模型建立及回归分析,选择合适的参考产品,得到新产品预测模型,以进行新产品良率预测。本发明中的方法依据制造工厂现有生产线上的成熟产品数据库作为良率预测模型的基础,数据真实可靠,能达到比较精确的新产品良率预测值,反应该半导体晶圆制造工厂的实际生产能力,可以根据不同工厂的产品数据库建立对应的良率预测模型,对半导体晶圆制造工厂有普适性。According to the yield prediction method in integrated circuit wafer manufacturing involved in this embodiment, based on the functional relationship between random defect density and yield in wafer manufacturing, refer to the yield data of mature products on the production line, and use the data model Establish and regression analysis, select appropriate reference products, and obtain new product prediction models to predict the yield of new products. The method in the present invention is based on the mature product database on the existing production line of the manufacturing plant as the basis of the yield prediction model. The data is true and reliable, and can reach a relatively accurate yield prediction value for new products, reflecting the actual production of the semiconductor wafer manufacturing plant Ability to establish a corresponding yield prediction model based on the product database of different factories, which is universal for semiconductor wafer manufacturing factories.
上述实施方式为本发明的优选案例,并不用来限制本发明的保护范围。The foregoing embodiments are preferred cases of the present invention, and are not used to limit the protection scope of the present invention.
上述本发明实施例序号仅仅为了描述,不代表实施例的优劣。The sequence numbers of the foregoing embodiments of the present invention are only for description, and do not represent the superiority or inferiority of the embodiments.
以上仅为本发明的优选实施例,并非因此限制本发明的专利范围,凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换,或直接或间接运用在其他相关的技术领域,均同理包括在本发明的专利保护范围内。The above are only the preferred embodiments of the present invention, and do not limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the description and drawings of the present invention, or directly or indirectly applied to other related technical fields , The same reason is included in the scope of patent protection of the present invention.
Claims (10)
- 一种集成电路晶圆制造中的良率预测方法,其特征在于,包括以下步骤:A method for predicting yield in integrated circuit wafer manufacturing, which is characterized in that it comprises the following steps:备选参考产品模型获取:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程;Obtaining candidate reference product models: selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;备选参考产品参数获取:分别获取各所述备选参考产品的各良率影响因子;Obtaining the parameters of the candidate reference products: respectively obtaining the yield impact factors of each of the candidate reference products;备选参考产品函数获取:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型;Obtaining alternative reference product functions: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate reference product Modified prediction yield model;备选参考产品预测:由各所述基于当前备选参考产品的修正预测良率模型,得到各其他备选参考产品的预测良率,根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值,根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数;Candidate reference product prediction: From each of the revised predicted yield models based on the current candidate reference product, the predicted yield of each other candidate reference product is obtained, and the predicted yield of each other candidate reference product is obtained according to the second preset rule. The overall yield error value of the product is obtained according to the third preset rule based on the correlation coefficient of the linear function of the full coordination factor of each candidate reference product;最终参考产品选择:选取所述整体良率误差值最小或全协调因子线性函数相关系数最接近1的备选参考产品作为最终参考产品;Final reference product selection: select the candidate reference product with the smallest overall yield error value or the linear function correlation coefficient of the full coordination factor closest to 1 as the final reference product;新产品预测模型获取:根据基于所述最终参考产品的所述全调整因子线性函数,得到基于所述最终参考产品的新产品预测模型;New product prediction model acquisition: obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;新产品良率预测:根据所述新产品良率预测模型以及所述新产品的良率影响因子,得到新产品良率预测结果。New product yield prediction: According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
- 根据权利要求1所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述第一预设规则为:The method for predicting yield in integrated circuit wafer manufacturing according to claim 1, wherein the first preset rule is:其中,Y e为所述备选参考产品e的实际良率, Where Ye is the actual yield of the candidate reference product e,λ e为对应所述备选参考产品e的晶圆平均缺陷数。 λ e is the average number of defects on the wafer corresponding to the candidate reference product e.
- 根据权利要求2所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述良率影响因子包含所述参考产品的光罩层数、晶片面积、最小线宽。The method for predicting yield in integrated circuit wafer manufacturing according to claim 2, wherein the yield influencing factor includes the number of mask layers, chip area, and minimum line width of the reference product.
- 根据权利要求3所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述全调整因子线性回归方程σ为:The yield prediction method in integrated circuit wafer manufacturing according to claim 3, wherein the linear regression equation σ of the full adjustment factor is:σ=-ln Y/λ e=f(X 1,X 2,X 3,···)=b+k 1X 1+k 2X 2+k 3X 3+··· σ=-ln Y/λ e = f(X 1 ,X 2 ,X 3 ,···)=b+k 1 X 1 +k 2 X 2 +k 3 X 3 +···其中,b为常数项,Among them, b is a constant term,X 1、X 2、X 3分别为与各良率影响因子相关的变量项 X 1 , X 2 , X 3 are the variable items related to the yield influencing factors, respectively为当前产品的光罩层数N与参考产品的光罩层数N e之比对应的变量项, E N ratio corresponding to the variable terms reticle mask layers current product and the reference product of the number of layers N,为当前产品的晶片面积A与参考产品的晶片面积A e之比对应的变量项, Is the variable item corresponding to the ratio of the chip area A of the current product to the chip area A e of the reference product,为参考产品的最小线宽W e与当前产品的最小线宽W之比对应的变量项, Is the variable item corresponding to the ratio of the minimum line width W e of the reference product to the minimum line width W of the current product,k 1为变量X 1对应的线性回归系数, k 1 is the linear regression coefficient corresponding to the variable X 1,k 2为变量X 2对应的线性回归系数, k 2 is the linear regression coefficient corresponding to the variable X 2,k 3为变量X 3对应的线性回归系数。 k 3 is the linear regression coefficient corresponding to the variable X 3.
- 根据权利要求1所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述修正良率预测模型为:The yield prediction method in integrated circuit wafer manufacturing according to claim 1, wherein the modified yield prediction model is:其中, 为产品i基于所述备选参考产品e的预测良率, in, Is the predicted yield of product i based on the candidate reference product e,σ i为产品i基于所述备选参考产品e的全调整因子, σ i is the full adjustment factor of product i based on the candidate reference product e,Y e为所述备选参考产品e的实际生产良率。 Ye is the actual production yield of the candidate reference product e.
- 根据权利要求6所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述第二预设规则为:基于当前备选参考产品时的整体良率误差值d为The yield prediction method in integrated circuit wafer manufacturing according to claim 6, wherein the second preset rule is: the overall yield error value d based on the current candidate reference product is其中,N为所述 其他备选参考产品总数量, Wherein, N is the total number of alternatives which he reference product,z i为其他备选参考产品i的预测良率 与实际良率Y i差值, z i is the predicted yield of other candidate reference products i And the actual yield Y i difference,μ为所有所述其他备选参考产品的各预测良率与实际良率的差值的总平均值。μ is the total average value of the difference between the predicted yield and the actual yield of all the other candidate reference products.
- 根据权利要求7所述的集成电路晶圆制造中的良率预测方法,其特征在于,所述第三预设规则包括:8. The yield prediction method in integrated circuit wafer manufacturing according to claim 7, wherein the third preset rule comprises:基于各所述备选参考产品的全协调因子线性函数相关系数R为Based on the full coordination factor linear function correlation coefficient R of each candidate reference product is
- 一种电子装置,其特征在于,该电子装置包括:存储器、处理器,所述存储器上存储有集成电路晶圆制造中的良率预测程序,所述集成电路晶圆制造中的良率预测程序被所述处理器执行时实现以下步骤:An electronic device, characterized in that it includes a memory and a processor, the memory stores a yield prediction program in integrated circuit wafer manufacturing, and the yield prediction program in integrated circuit wafer manufacturing When executed by the processor, the following steps are implemented:备选参考产品模型获取:选取参考产品库中的第一数量个备选参考产品,根据第一预设规则,得到基于各所述备选参考产品的全调整因子线性回归方程;Obtaining candidate reference product models: selecting the first number of candidate reference products in the reference product library, and obtaining the full adjustment factor linear regression equation based on each of the candidate reference products according to the first preset rule;备选参考产品参数获取:分别获取各所述备选参考产品的各良率影响因子;Obtaining the parameters of the candidate reference products: respectively obtaining the yield impact factors of each of the candidate reference products;备选参考产品函数获取:根据所述各良率影响因子和所述全调整因子线性回归方程,分别得到各所述备选参考产品的全协调因子线性函数,以及基于各所述备选参考产品的修正预测良率模型;Obtaining alternative reference product functions: According to the respective yield influencing factors and the full adjustment factor linear regression equation, the full coordination factor linear function of each candidate reference product is obtained, and based on each candidate reference product Modified prediction yield model;备选参考产品预测模块:由各所述基于当前备选参考产品的修正预测良率模型,得到各其他备选参考产品的预测良率,根据第二预设规则分别得到基于各所述备选参考产品的整体良率误差值,根据第三预设规则得到基于各所述备选参考产品的全协调因子线性函数相关系数;Alternative reference product prediction module: From each of the revised predicted yield models based on the current alternative reference products, the predicted yields of other alternative reference products are obtained, and the predicted yields of other alternative reference products are obtained according to the second preset rule. The overall yield error value of the reference product is obtained, and the correlation coefficient of the linear function based on the full coordination factor of each candidate reference product is obtained according to the third preset rule;最终参考产品选择模块:选取所述整体良率误差值最小的备选参考产品作为最终参考产品;Final reference product selection module: select the candidate reference product with the smallest overall yield error value as the final reference product;新产品预测模型获取:根据基于所述最终参考产品的所述全调整因子线性 函数,得到基于所述最终参考产品的新产品预测模型;Obtaining a new product prediction model: obtaining a new product prediction model based on the final reference product according to the full adjustment factor linear function based on the final reference product;新产品良率预测:根据所述新产品良率预测模型以及所述新产品的良率影响因子,得到新产品良率预测结果。New product yield prediction: According to the new product yield prediction model and the yield influence factor of the new product, a new product yield prediction result is obtained.
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质上存储有所述集成电路晶圆制造中的良率预测程序,所述集成电路晶圆制造中的良率预测程序被处理器执行时实现如权利要求1至9中任意一项所述的集成电路晶圆制造中的良率预测方法的步骤。A computer-readable storage medium, wherein the computer-readable storage medium stores the yield prediction program in the integrated circuit wafer manufacturing, and the yield prediction program in the integrated circuit wafer manufacturing is The processor implements the steps of the method for predicting yield in integrated circuit wafer manufacturing according to any one of claims 1 to 9 when executed.
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